{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 正确的log输出 60分。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python3\n",
    "import numpy as np\n",
    "\n",
    "samples_and = [\n",
    "    [0, 0, 0],\n",
    "    [1, 0, 0],\n",
    "    [0, 1, 0],\n",
    "    [1, 1, 1],\n",
    "]\n",
    "\n",
    "samples_or = [\n",
    "    [0, 0, 0],\n",
    "    [1, 0, 1],\n",
    "    [0, 1, 1],\n",
    "    [1, 1, 1],\n",
    "]\n",
    "\n",
    "samples_xor = [\n",
    "    [0, 0, 0],\n",
    "    [1, 0, 1],\n",
    "    [0, 1, 1],\n",
    "    [1, 1, 0],\n",
    "]\n",
    "\n",
    "def perceptron(samples):\n",
    "    w = np.array([1, 2])\n",
    "    b = 0\n",
    "    a = 1\n",
    "\n",
    "    print('epoch {:3s} sample {}  [{} {} {:3s} {:3s} {:3s} {:3s} {:3s}]'.format(\n",
    "                \"  i\", \"j\", \"w[0]\", \"w[1]\", \" b\", \" y\", \"delta_w[0]\", \"delta_w[1]\", \"delta_b\"))\n",
    "    \n",
    "    for i in range(10):\n",
    "        for j in range(4):\n",
    "            x = np.array(samples[j][:2])\n",
    "            y = 1 if np.dot(w, x) + b > 0 else 0\n",
    "            d = np.array(samples[j][2])\n",
    "\n",
    "            delta_b = a*(d-y)\n",
    "            delta_w = a*(d-y)*x\n",
    "\n",
    "            print('epoch {:3d} sample {}  [{:3d} {:4d} {:3d} {:3d} {:10d} {:10d} {:8d}]'.format(\n",
    "                i, j, w[0], w[1], b, y, delta_w[0], delta_w[1], delta_b\n",
    "            ))\n",
    "            w = w + delta_w\n",
    "            b = b + delta_b\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------------\n",
      "Logical AND\n",
      "epoch   i sample j  [w[0] w[1]  b   y  delta_w[0] delta_w[1] delta_b]\n",
      "epoch   0 sample 0  [  1    2   0   0          0          0        0]\n",
      "epoch   0 sample 1  [  1    2   0   1         -1          0       -1]\n",
      "epoch   0 sample 2  [  0    2  -1   1          0         -1       -1]\n",
      "epoch   0 sample 3  [  0    1  -2   0          1          1        1]\n",
      "epoch   1 sample 0  [  1    2  -1   0          0          0        0]\n",
      "epoch   1 sample 1  [  1    2  -1   0          0          0        0]\n",
      "epoch   1 sample 2  [  1    2  -1   1          0         -1       -1]\n",
      "epoch   1 sample 3  [  1    1  -2   0          1          1        1]\n",
      "epoch   2 sample 0  [  2    2  -1   0          0          0        0]\n",
      "epoch   2 sample 1  [  2    2  -1   1         -1          0       -1]\n",
      "epoch   2 sample 2  [  1    2  -2   0          0          0        0]\n",
      "epoch   2 sample 3  [  1    2  -2   1          0          0        0]\n",
      "epoch   3 sample 0  [  1    2  -2   0          0          0        0]\n",
      "epoch   3 sample 1  [  1    2  -2   0          0          0        0]\n",
      "epoch   3 sample 2  [  1    2  -2   0          0          0        0]\n",
      "epoch   3 sample 3  [  1    2  -2   1          0          0        0]\n",
      "epoch   4 sample 0  [  1    2  -2   0          0          0        0]\n",
      "epoch   4 sample 1  [  1    2  -2   0          0          0        0]\n",
      "epoch   4 sample 2  [  1    2  -2   0          0          0        0]\n",
      "epoch   4 sample 3  [  1    2  -2   1          0          0        0]\n",
      "epoch   5 sample 0  [  1    2  -2   0          0          0        0]\n",
      "epoch   5 sample 1  [  1    2  -2   0          0          0        0]\n",
      "epoch   5 sample 2  [  1    2  -2   0          0          0        0]\n",
      "epoch   5 sample 3  [  1    2  -2   1          0          0        0]\n",
      "epoch   6 sample 0  [  1    2  -2   0          0          0        0]\n",
      "epoch   6 sample 1  [  1    2  -2   0          0          0        0]\n",
      "epoch   6 sample 2  [  1    2  -2   0          0          0        0]\n",
      "epoch   6 sample 3  [  1    2  -2   1          0          0        0]\n",
      "epoch   7 sample 0  [  1    2  -2   0          0          0        0]\n",
      "epoch   7 sample 1  [  1    2  -2   0          0          0        0]\n",
      "epoch   7 sample 2  [  1    2  -2   0          0          0        0]\n",
      "epoch   7 sample 3  [  1    2  -2   1          0          0        0]\n",
      "epoch   8 sample 0  [  1    2  -2   0          0          0        0]\n",
      "epoch   8 sample 1  [  1    2  -2   0          0          0        0]\n",
      "epoch   8 sample 2  [  1    2  -2   0          0          0        0]\n",
      "epoch   8 sample 3  [  1    2  -2   1          0          0        0]\n",
      "epoch   9 sample 0  [  1    2  -2   0          0          0        0]\n",
      "epoch   9 sample 1  [  1    2  -2   0          0          0        0]\n",
      "epoch   9 sample 2  [  1    2  -2   0          0          0        0]\n",
      "epoch   9 sample 3  [  1    2  -2   1          0          0        0]\n"
     ]
    }
   ],
   "source": [
    "    print(\"-\"*60)\n",
    "    print('Logical AND')\n",
    "    perceptron(samples_and)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 逻辑与: 可以看到，最终在 epoch2 的样本 2, 3 上，参数就已经停⽌更新了，接下来的 epoch3 以后，参数完全没有更新。最终得到的参数为 w1=1, w2=2, b=-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------------\n",
      "Logical OR\n",
      "epoch   i sample j  [w[0] w[1]  b   y  delta_w[0] delta_w[1] delta_b]\n",
      "epoch   0 sample 0  [  1    2   0   0          0          0        0]\n",
      "epoch   0 sample 1  [  1    2   0   1          0          0        0]\n",
      "epoch   0 sample 2  [  1    2   0   1          0          0        0]\n",
      "epoch   0 sample 3  [  1    2   0   1          0          0        0]\n",
      "epoch   1 sample 0  [  1    2   0   0          0          0        0]\n",
      "epoch   1 sample 1  [  1    2   0   1          0          0        0]\n",
      "epoch   1 sample 2  [  1    2   0   1          0          0        0]\n",
      "epoch   1 sample 3  [  1    2   0   1          0          0        0]\n",
      "epoch   2 sample 0  [  1    2   0   0          0          0        0]\n",
      "epoch   2 sample 1  [  1    2   0   1          0          0        0]\n",
      "epoch   2 sample 2  [  1    2   0   1          0          0        0]\n",
      "epoch   2 sample 3  [  1    2   0   1          0          0        0]\n",
      "epoch   3 sample 0  [  1    2   0   0          0          0        0]\n",
      "epoch   3 sample 1  [  1    2   0   1          0          0        0]\n",
      "epoch   3 sample 2  [  1    2   0   1          0          0        0]\n",
      "epoch   3 sample 3  [  1    2   0   1          0          0        0]\n",
      "epoch   4 sample 0  [  1    2   0   0          0          0        0]\n",
      "epoch   4 sample 1  [  1    2   0   1          0          0        0]\n",
      "epoch   4 sample 2  [  1    2   0   1          0          0        0]\n",
      "epoch   4 sample 3  [  1    2   0   1          0          0        0]\n",
      "epoch   5 sample 0  [  1    2   0   0          0          0        0]\n",
      "epoch   5 sample 1  [  1    2   0   1          0          0        0]\n",
      "epoch   5 sample 2  [  1    2   0   1          0          0        0]\n",
      "epoch   5 sample 3  [  1    2   0   1          0          0        0]\n",
      "epoch   6 sample 0  [  1    2   0   0          0          0        0]\n",
      "epoch   6 sample 1  [  1    2   0   1          0          0        0]\n",
      "epoch   6 sample 2  [  1    2   0   1          0          0        0]\n",
      "epoch   6 sample 3  [  1    2   0   1          0          0        0]\n",
      "epoch   7 sample 0  [  1    2   0   0          0          0        0]\n",
      "epoch   7 sample 1  [  1    2   0   1          0          0        0]\n",
      "epoch   7 sample 2  [  1    2   0   1          0          0        0]\n",
      "epoch   7 sample 3  [  1    2   0   1          0          0        0]\n",
      "epoch   8 sample 0  [  1    2   0   0          0          0        0]\n",
      "epoch   8 sample 1  [  1    2   0   1          0          0        0]\n",
      "epoch   8 sample 2  [  1    2   0   1          0          0        0]\n",
      "epoch   8 sample 3  [  1    2   0   1          0          0        0]\n",
      "epoch   9 sample 0  [  1    2   0   0          0          0        0]\n",
      "epoch   9 sample 1  [  1    2   0   1          0          0        0]\n",
      "epoch   9 sample 2  [  1    2   0   1          0          0        0]\n",
      "epoch   9 sample 3  [  1    2   0   1          0          0        0]\n"
     ]
    }
   ],
   "source": [
    "    print(\"-\"*60)\n",
    "    print('Logical OR')\n",
    "    perceptron(samples_or) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 逻辑或: 这⾥⽐较巧合，初始化使⽤的参数，正好满⾜这⾥的数据 w1=1, w2=2, b=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------------\n",
      "Logical XOR\n",
      "epoch   i sample j  [w[0] w[1]  b   y  delta_w[0] delta_w[1] delta_b]\n",
      "epoch   0 sample 0  [  1    2   0   0          0          0        0]\n",
      "epoch   0 sample 1  [  1    2   0   1          0          0        0]\n",
      "epoch   0 sample 2  [  1    2   0   1          0          0        0]\n",
      "epoch   0 sample 3  [  1    2   0   1         -1         -1       -1]\n",
      "epoch   1 sample 0  [  0    1  -1   0          0          0        0]\n",
      "epoch   1 sample 1  [  0    1  -1   0          1          0        1]\n",
      "epoch   1 sample 2  [  1    1   0   1          0          0        0]\n",
      "epoch   1 sample 3  [  1    1   0   1         -1         -1       -1]\n",
      "epoch   2 sample 0  [  0    0  -1   0          0          0        0]\n",
      "epoch   2 sample 1  [  0    0  -1   0          1          0        1]\n",
      "epoch   2 sample 2  [  1    0   0   0          0          1        1]\n",
      "epoch   2 sample 3  [  1    1   1   1         -1         -1       -1]\n",
      "epoch   3 sample 0  [  0    0   0   0          0          0        0]\n",
      "epoch   3 sample 1  [  0    0   0   0          1          0        1]\n",
      "epoch   3 sample 2  [  1    0   1   1          0          0        0]\n",
      "epoch   3 sample 3  [  1    0   1   1         -1         -1       -1]\n",
      "epoch   4 sample 0  [  0   -1   0   0          0          0        0]\n",
      "epoch   4 sample 1  [  0   -1   0   0          1          0        1]\n",
      "epoch   4 sample 2  [  1   -1   1   0          0          1        1]\n",
      "epoch   4 sample 3  [  1    0   2   1         -1         -1       -1]\n",
      "epoch   5 sample 0  [  0   -1   1   1          0          0       -1]\n",
      "epoch   5 sample 1  [  0   -1   0   0          1          0        1]\n",
      "epoch   5 sample 2  [  1   -1   1   0          0          1        1]\n",
      "epoch   5 sample 3  [  1    0   2   1         -1         -1       -1]\n",
      "epoch   6 sample 0  [  0   -1   1   1          0          0       -1]\n",
      "epoch   6 sample 1  [  0   -1   0   0          1          0        1]\n",
      "epoch   6 sample 2  [  1   -1   1   0          0          1        1]\n",
      "epoch   6 sample 3  [  1    0   2   1         -1         -1       -1]\n",
      "epoch   7 sample 0  [  0   -1   1   1          0          0       -1]\n",
      "epoch   7 sample 1  [  0   -1   0   0          1          0        1]\n",
      "epoch   7 sample 2  [  1   -1   1   0          0          1        1]\n",
      "epoch   7 sample 3  [  1    0   2   1         -1         -1       -1]\n",
      "epoch   8 sample 0  [  0   -1   1   1          0          0       -1]\n",
      "epoch   8 sample 1  [  0   -1   0   0          1          0        1]\n",
      "epoch   8 sample 2  [  1   -1   1   0          0          1        1]\n",
      "epoch   8 sample 3  [  1    0   2   1         -1         -1       -1]\n",
      "epoch   9 sample 0  [  0   -1   1   1          0          0       -1]\n",
      "epoch   9 sample 1  [  0   -1   0   0          1          0        1]\n",
      "epoch   9 sample 2  [  1   -1   1   0          0          1        1]\n",
      "epoch   9 sample 3  [  1    0   2   1         -1         -1       -1]\n"
     ]
    }
   ],
   "source": [
    "    print(\"-\"*60)\n",
    "    print('Logical XOR')\n",
    "    perceptron(samples_xor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 逻辑异或: 可以看到，在进入 epoch5 后，*参数就已经出现反复循环*，*无法得到最终稳定的参数*。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 解释为什么这⾥的感知器代码⽆法完成异或功能 40分。"
   ]
  },
  {
   "attachments": {
    "how_to_solve_xor.JPG": {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "逻辑异或问题的真值表如下：\n",
    "\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>X1</th>\n",
    "<th>X2</th>\n",
    "<th>OUT</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody>\n",
    "<tr>\n",
    "<td>0</td>\n",
    "<td>0</td>\n",
    "<td>0</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>1</td>\n",
    "<td>0</td>\n",
    "<td>1</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>0</td>\n",
    "<td>1</td>\n",
    "<td>1</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>1</td>\n",
    "<td>1</td>\n",
    "<td>0</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "就是说两个不一样，就是真（1），如果把逻辑异或问题表现为二维的分布，就是这样\n",
    "\n",
    "![xor_diagram.JPG](attachment:xor_diagram.JPG)\n",
    "\n",
    "这样就很直观的把逻辑异或问题表现为点在二维平面上分布的问题。\n",
    "\n",
    "感知机的作用就是就是在一个超平面上画一条线，线的一边为一类。如果感知机只有两个输入，就是在二维平面上，划线然后分类。\n",
    "\n",
    "如上图所示，在”逻辑异或“问题上找不到一条直线能把红点和黑点分开，也就是说这是一个不能用直线分类的问题，这类就叫线性不可分问题。同理，“逻辑同或”问题一样不能解决。如果是“逻辑与”或“逻辑或”问题就可以解决。\n",
    "\n",
    "如果想解决这个逻辑异或问题，至少需要再加一层感知器，如下图所示。\n",
    "\n",
    "![how_to_solve_xor.JPG](attachment:how_to_solve_xor.JPG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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